Animal behavior isn't complicated, but it is complex. Nicolas Perony studies how individual animals -- be they Scottish Terriers, bats or meerkats -- follow simple rules that, collectively, create larger patterns of behavior. And how this complexity born of simplicity can help them adapt to new circumstances, as they arise.

Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.

(Illustrative videos can be downloaded from here:this http URL)

Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networksPeter Csermely

César visits the RSA to present a new view of the relationship between individual and collective knowledge, linking information theory, economics and biology to explain the deep evolution of social and economic systems.In a radical rethink of what an economy is, one of WIRED magazine’s 50 People Who Could Change the World, César Hidalgo argues that it is the measure of a nation’s cultural complexity – the nexus of people, ideas and invention - rather than its GDP or per-capita income, that explains the success or failure of its economic performance. To understand the growth of economies, Hidalgo argues, we first need to understand the growth of order itself.

Economics is changing. In the last few years it has generated a number of new approaches. One of the most promising - complexity economics - was pioneered in the 1980s and 1990s by a small team at the Santa Fe Institute. Economist and complexity theorist W. Brian Arthur led that team, and in this book he collects many of his articles on this new approach. The traditional framework sees behavior in the economy as in an equilibrium steady state. People in the economy face well-defined problems and use perfect deductive reasoning to base their actions on. The complexity framework, by contrast, sees the economy as always in process, always changing. People try to make sense of the situations they face using whatever reasoning they have at hand, and together create outcomes they must individually react to anew. The resulting economy is not a well-ordered machine, but a complex evolving system that is imperfect, perpetually constructing itself anew, and brimming with vitality.

The new vision complements and widens the standard one, and it helps answer many questions: Why does the stock market show moods and a psychology? Why do high-tech markets tend to lock in to the dominance of one or two very large players? How do economies form, and how do they continually alter in structure over time?

The papers collected here were among the first to use evolutionary computation, agent-based modeling, and cognitive psychology. They cover topics as disparate as how markets form out of beliefs; how technology evolves over the long span of time; why systems and bureaucracies get more complicated as they evolve; and how financial crises can be foreseen and prevented in the future.

The Western Ghats in India rise like a wall between the Arabian Sea and the heart of the subcontinent to the east. The 1,000-mile-long chain of coastal mountains is dense with lush rainforest and grasslands, and each year, clouds bearing monsoon rains blow in from the southwest and break against the mountains’ flanks, unloading water…

Nice multi-agent experiment showing the emergence of friendliness and the thinking on mode other's, after all a human advantage, neurocientist explain it by mirror neurons. Is the ultimate reason for the existence of Facebook and such.

This article is an attempt to capture, in a reasonable space, some of the major developments and currents of thought in information theory and the relations between them. I have particularly tried to include changes in the views of key authors in the field. The domains addressed range from mathematical-categorial, philosophical and computational approaches to systems, causal-compositional, biological and religious approaches and messaging theory. I have related key concepts in each domain to my non-standard extension of logic to real processes that I call Logic in Reality (LIR). The result is not another attempt at a General Theory of Information such as that of Burgin, or a Unified Theory of Information like that of Hofkirchner. It is not a compendium of papers presented at a conference, more or less unified around a particular theme. It is rather a highly personal, limited synthesis which nonetheless may facilitate comparison of insights, including contradictory ones, from different lines of inquiry. As such, it may be an example of the concept proposed by Marijuan, still little developed, of the recombination of knowledge. Like the best of the work to which it refers, the finality of this synthesis is the possible contribution that an improved understanding of the nature and dynamics of information may make to the ethical development of the information society.

Brenner and Daniel Cohnitz have a very good book about the subject "Information and Information Flow" that covers almost all aspects of Information Theory. Unfortunatelly the 'Matecmatical Information Theory' of Jan Kahre didn't have yet the same attention.

All information that we receive from the universe that is around us is second hand. It is possible to alter and shift them out of our own volition or of the volition of someone else, provided that we're either caught unawares or allowing it to happen just as it is theoretically possible to shift the universe around us, so that we experience something different than what would ordinarily happen (again, only theoretically, not necessarily in actuality). The universe is out there, I think, just as we're most certainly apart of it. There are laws to this place as well which influence and effect our abilities to act, our perception of the choices that we have and the choices that we actually are left with at the end of the day, when all's said and told. We are just receptors, analyzers and synthesizers of information with our biological bodies. We are all slaves, ultimately, to our biology, our circumstances and the consequences of our actions.

Decisions in a group often result in imitation and aggregation, which are enhanced in panic, dangerous, stressful or negative situations. Current explanations of this enhancement are restricted to particular contexts, such as anti-predatory behavior, deflection of responsibility in humans, or cases in which the negative situation is associated with an increase in uncertainty. But this effect is observed across taxa and in very diverse conditions, suggesting that it may arise from a more general cause, such as a fundamental characteristic of social decision-making. Current decision-making theories do not explain it, but we noted that they concentrate on estimating which of the available options is the best one, implicitly neglecting the cases in which several options can be good at the same time. We explore a more general model of decision-making that instead estimates the probability that each option is good, allowing several options to be good simultaneously. This model predicts with great generality the enhanced imitation in negative situations. Fish and human behavioral data showing an increased imitation behavior in negative circumstances are well described by this type of decisions to choose a good option.

The Informative Herd: why humans and other animals imitate more when conditions are adverseAlfonso Pérez-Escudero, Gonzalo G. de Polavieja

There is a rapidly expanding literature on the application of complex networks in economics that focused mostly on stock markets. In this paper, we discuss an application of complex networks to study international business cycles.

Methodology/Principal Findings

We construct complex networks based on GDP data from two data sets on G7 and OECD economies. Besides the well-known correlation-based networks, we also use a specific tool for presenting causality in economics, the Granger causality. We consider different filtering methods to derive the stationary component of the GDP series for each of the countries in the samples. The networks were found to be sensitive to the detrending method. While the correlation networks provide information on comovement between the national economies, the Granger causality networks can better predict fluctuations in countries’ GDP. By using them, we can obtain directed networks allows us to determine the relative influence of different countries on the global economy network. The US appears as the key player for both the G7 and OECD samples.

These are the natural laws and connections which exist amongst various economies and within each economy. This shows the interconnectedness of the whole planet's economy and can give predictions as to what could happen if one particular economy were to crash and fall into valuelessness for humanity.

It's interesting that this research comes at a time in our history when the natural laws of social interactions are being violated by governments and elite groups everywhere. What will happen if discontent turns into unrest and rebellions in the United States? What happens if the authority of governments ceases to be legitimate, to the point where violence and anarchy take their place. What will happen to the economy if the rule of law is no longer abided, and the mob takes over to deal with the perceived injustices that the elite groups have committed against the general public?

What happens when the environment gives way and our societies are no longer able to support the populations that are present? What happens when people are forced to either starve or fight?

That's the direction that we're headed towards, I'm afraid.

Funny how it is that the conservatives from all parties who enacted these policies, are leading to the very destruction of society that they're so afraid of. Funny how it is that things get more delicate and likely to change significantly as they cling to their image of how the past was (and it is just an image of the past, not the real world as it was, is or will be).

The modern world is complex beyond human understanding and control. The science of complex systems aims to find new ways of thinking about the many interconnected networks of interaction that defy traditional approaches. Thus far, research into networks has largely been restricted to pairwise relationships represented by links between two nodes. This volume marks a major extension of networks to multidimensional hypernetworks for modeling multi-element relationships, such as companies making up the stock market, the neighborhoods forming a city, people making up committees, divisions making up companies, computers making up the internet, men and machines making up armies, or robots working as teams.

This volume makes an important contribution to the science of complex systems by: (i) extending network theory to include dynamic relationships between many elements; (ii) providing a mathematical theory able to integrate multilevel dynamics in a coherent way; (iii) providing a new methodological approach to analyze complex systems; and (iv) illustrating the theory with practical examples in the design, management and control of complex systems taken from many areas of application.

Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using a random sample of Twitter users, whose activity (and the stimuli they were exposed to) was observed during a week of September 2014. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.

The Doctoral Program in Complexity Sciences provides an integrated training that enable doctoral students understand the environment in which they live, by applying modelling methods and computer simulation, and solve complex problems using information technology, including support systems to organizational processes in complex environments. Developing these skills will enable the integration of multidisciplinary knowledge and the autonomously formulation of judgements from data that is often incomplete.

The Doctoral Program in Complexity Sciences is taught in ISCTE and FCUL. It has an international dimension based on a set of protocols to the Paris-Dauphine University (France), with the University of Savoie (France) and the Academy of Economic Studies of Bucharest (Romania). There are teachers of exchanges with the University Paul Sabatier in Toulouse (France), with the Open University (UK), with the University of Utrecht (Netherlands) and the University of Texas (USA).

The new curriculum comprehends a 1st curricular year and a 2nd and 3rd years mainly dedicated to research at PhD level. Students are invited to develop their research projects at LabMAg (FCUL, Lisbon), ISTAR (ISCTE-IUL, Lisbon), and IITGn (Gandhinagar, India).

A new class for the 1st curricular year will start in February 2016. Classes will take place on Tuesdays and Wednesdays, from 18h to 21h30.

Applications for the 1st curricular year are open until the 23rd of December, 2015. Applications are submitted through the form available at the ISCTE-IUL applications website.

Memes were originally framed in relationship to genes. In The Selfish Gene, Dawkins claimed that humans are “survival machines” for our genes, the replicating molecules that emerged from the primordial soup and that, through mutation and natural selection, evolved to generate beings that were more effective as carriers and propagators of genes. Still, Dawkins explained, genes could not account for all of human behavior, particularly the evolution of cultures. So he identified a second replicator, a “unit of cultural transmission” that he believed was “leaping from brain to brain” through imitation. He named these units “memes,” an adaption of the Greek word mimene, “to imitate.”Dawkins’ memes include everything from ideas, songs, and religious ideals to pottery fads. Like genes, memes mutate and evolve, competing for a limited resource—namely, our attention. Memes are, in Dawkins’ view, viruses of the mind—infectious. The successful ones grow exponentially, like a super flu. While memes are sometimes malignant (hellfire and faith, for atheist Dawkins), sometimes benign (catchy songs), and sometimes terrible for our genes (abstinence), memes do not have conscious motives. But still, he claims, memes parasitize us and drive us.

Systems of many interacting components — be they species, integers or subatomic particles — kept producing the same statistical curve, which had become known as the Tracy-Widom distribution. This puzzling curve seemed to be the complex cousin of the familiar bell curve, or Gaussian distribution, which represents the natural variation of independent random variables like the heights of students in a classroom or their test scores. Like the Gaussian, the Tracy-Widom distribution exhibits “universality,” a mysterious phenomenon in which diverse microscopic effects give rise to the same collective behavior. “The surprise is it’s as universal as it is,” said Tracy, a professor at the University of California, Davis.

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Here we propose a simple formula for describing the characteristic properties of memes in the scientific literature, which is based on their frequency of occurrence and the degree to which they propagate along the citation graph. The product of the frequency and the propagation degree is the meme score, which accurately identifies important and interesting memes within a scientific field. We use data from close to 50 million publication records from the Web of Science, PubMed Central and the American Physical Society to demonstrate the effectiveness of our approach. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative metrics confirm that the meme score is highly effective, while requiring no external resources or arbitrary thresholds and filters.

António F Fonseca's insight:

A simple truth: memes are repetition and imitation. A nice paper based on this simple idea.

Social networks have many counter-intuitive properties, including the "friendship paradox" that states, on average, your friends have more friends than you do. Recently, a variety of other paradoxes were demonstrated in online social networks. This paper explores the origins of these network paradoxes. Specifically, we ask whether they arise from mathematical properties of the networks or whether they have a behavioral origin. We show that sampling from heavy-tailed distributions always gives rise to a paradox in the mean, but not the median. We propose a strong form of network paradoxes, based on utilizing the median, and validate it empirically using data from two online social networks. Specifically, we show that for any user the majority of user's friends and followers have more friends, followers, etc. than the user, and that this cannot be explained by statistical properties of sampling. Next, we explore the behavioral origins of the paradoxes by using the shuffle test to remove correlations between node degrees and attributes. We find that paradoxes for the mean persist in the shuffled network, but not for the median. We demonstrate that strong paradoxes arise due to the assortativity of user attributes, including degree, and correlation between degree and attribute.

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

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